Butterknife

1. createcounttable.R

Explanation

Create the input data for ribodiff.

Call

Rscript createcounttable.R -i data_path -s singal_name -b background_name -c conbditions (comma separated, e.g.: Wildtype,Mutant) -r number_of_replicates -o output_path

Input

Count data from htseq-count.

2. Ribodiff

Explanation

Ribodiff allows to call differentially translated regions across two different conditions, incorporating Control data.

Install

• Ribodiff can be found here. All credits go for the developers; published in Zhong et al.
• Install ribodiff via bioconda in an virtual environment (ribodiff)
• activate env
• Clone Ribodiff from git
• Go into the scripts folder where TE.py is

Call

python2 TE.py -e conditionsheet -c counttable -o outputfile

Input

Data from createcounttable.R

3. Riborex

Explanation

Riborex allows to call differentially translated regions across two different conditions, incorporating Control data.

Install

• Riborex was published in Li et al.
• Install all packages stated in the header of riborex.R.

Call

Rscript riborex -i data_path -s singal_name -b background_name -c conbditions (comma separated, e.g.: Wildtype,Mutant) -r number_of_replicates -o output_path

Input

Count data from htseq-count.

4. ribodiff_riborex_compare.py

Explanation

Script filters results of ribodiff and riborex based on p-value threshold (<0.05) and compare the two lists of ribodiff and riborex to find regions metnioned in both lists.

Call

python ribodiff_riborex_compare.py

Input

Results of riborex.R and ribodiff.

5. annotate_gene_lists.sh

Explanation

Very small scripts to annotate the results of ribodiff_riborex_compare.py.

Call

annotate_gene_lists.sh regions_file annoation_file output_file

Input

Result of ribodiff_riborex_compare.py